In [1]}, the authors quantified the generalization gap based on the per-class feature means in the training and testing data using the squared Euclidean distance. Our generalization gap is similar to their feature deviation computation, except that we do not use the class mean of features. By utilizing the feature ranges (maximum and minimum), we are able to assess deviations in the footprints of the embedded classes. This is a critical concept in the context of imbalanced learning. In addition, we use the Manhattan distance with a floor (i.e., zero distance). The floor takes effect if the test feature embedding minimum or maximum falls within the training feature embedding range. The Manhattan distance has the advantage of being less sensitive to outliers and the floor prevents the generalization gap from being reduced when the test distribution falls within the training range.
